#
# Copyright 2016 The BigDL Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

import torch
import time
import argparse

from ipex_llm.transformers import AutoModelForCausalLM
# from transformers import LlamaTokenizer
from transformers import AutoTokenizer, GenerationConfig


PROMPT_FORMAT = """
A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think> <answer> answer here </answer>.
User: {prompt}.
Assistant: <think>
"""

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for DeepSeek V3/R1 model')
    parser.add_argument('--repo-id-or-model-path', type=str, default="unsloth/DeepSeek-R1-BF16",
                        help='The huggingface repo id for the DeepSeek V3/R1 (e.g. `unsloth/DeepSeek-R1-BF16`) to be downloaded'
                             ', or the path to the huggingface checkpoint folder')
    parser.add_argument('--prompt', type=str, default="What is AI?",
                        help='Prompt to infer')
    parser.add_argument('--n-predict', type=int, default=32,
                        help='Max tokens to predict')

    args = parser.parse_args()
    model_path = args.repo_id_or_model_path

    # Load model in 4 bit,
    # which convert the relevant layers in the model into INT4 format
    # When running LLMs on Intel iGPUs for Windows users, we recommend setting `cpu_embedding=True` in the from_pretrained function.
    # This will allow the memory-intensive embedding layer to utilize the CPU instead of iGPU.
    model = AutoModelForCausalLM.from_pretrained(model_path,
                                                 load_in_4bit=True,
                                                 optimize_model=True,
                                                 trust_remote_code=True,
                                                 use_cache=True)

    print(model)

    # Load tokenizer
    tokenizer = AutoTokenizer.from_pretrained(model_path,
                                              trust_remote_code=True)

    # Generate predicted tokens
    with torch.inference_mode():
        prompt = PROMPT_FORMAT.format(prompt=args.prompt)
        input_ids = tokenizer.encode(prompt, return_tensors="pt")
        # ipex_llm model needs a warmup, then inference time can be accurate
        output = model.generate(input_ids,
                                max_new_tokens=args.n_predict)

        # start inference
        st = time.time()
        # if your selected model is capable of utilizing previous key/value attentions
        # to enhance decoding speed, but has `"use_cache": false` in its model config,
        # it is important to set `use_cache=True` explicitly in the `generate` function
        # to obtain optimal performance with IPEX-LLM INT4 optimizations
        output = model.generate(input_ids,
                                max_new_tokens=args.n_predict)

        end = time.time()
        output = output.cpu()
        output_str = tokenizer.decode(output[0], skip_special_tokens=True)
        print(f'Inference time: {end-st} s')
        print('-'*20, 'Prompt', '-'*20)
        print(prompt)
        print('-'*20, 'Output', '-'*20)
        print(output_str)
